A Data Mining-Based Response Model for Target Selection in Direct Marketing

Abstract

Identifying customers who are more likely to respond to new product offers is an important issue in direct marketing. In direct marketing, data mining has been used extensively to identify potential customers for a new product (target selection). Using historical purchase data, a predictive response model with data mining techniques was developed to predict a probability that a customer in Ebedi Microfinance bank will respond to a promotion or an offer. To achieve this purpose, a predictive response model using customers' historical purchase data was built with data mining techniques. The data were stored in a data warehouse to serve as management decision support system. The response model was built from customers' historic purchases and demographic dataset. Bayesian algorithm precisely Naïve Bayes algorithm was employed in constructing the classifier system. Both filter and wrapper feature selection techniques were employed in determining inputs to the model. The results obtained shows that Ebedi Microfinance bank can plan effective marketing of their products and services by obtaining a guiding report on the status of their customers which will go a long way in assisting management in saving significant amount of money that could have been spent on wasteful promotional campaigns.

An Efficient Feature Selection based on Bayes Theorem, Self Information and Sequential Forward Selection

“…Performance of the classifier can be analyzed using the most widely used metrics viz., Accuracy, Precision and Recall [3]. Accuracy is the percentage of test tuples that are correctly classified by the classifier [3] [14]. Precision is a measure of exactness and Recall is a measure of completeness.…”

“…Where True Positive (TP) and True Negative (TN) refer to the positive and negative tuples that are correctly identified by the classifier respectively, False Positive (FP) and False Negative (FN) refer to the negative and positive tuples that are incorrectly classified by the classifier respectively [3] [14].…”

Bank Direct Marketing Analysis of Data Mining Techniques

“…The target of data mining is to find unexpected characteristics, hidden features or other unclear relationships in the data based on techniques' combination. Today, many applications in a wide and various ranges of business founded and worked in this regulation [7].…”

“…From the literature, the direct marketing is becoming a very important application in data mining these days. The data mining has been used widely in direct marketing to identify prospective customers for new products, by using purchasing data, a predictive model to measure that a customer is going to respond to the promotion or an offer [7].…”

“…The technique C5.0 will be discussed in section (6). The discussion of data and interpretation of it will be in section (7). Section (8) demonstrates the experimental results to show the effectiveness of each model, then the conclusion will present in (9).…”